What tools are used to deal with adversarial examples problem?
However, do industrial strength, production ready defensive strategies and approaches exist? Are there known examples of applied adversarial-resistant networks for one or more specific types (e.g. for small perturbation limits)?


I think it's difficult to tell whether or not there are any industrial strength defenses out there (which I assume would mean that they'd be reliable against all or most known methods of attacking). Adversarial Machine Learning is indeed a highly active, and growing, area of research. Not only are new approaches for defending being published quite regularly, but there is also active research into different approaches for "attacking". With new attack methods being discovered frequently, it's unlikely that anyone can already claim to have approaches that would work reliably against them all.


  The primary interest of this question, however, is whether any tools exist that can defend against some adversarial example attacks.


The closest thing to a ready-to-use "tool" that I've been able to find is IBM's Adversarial Robustness Toolbox, which appears to have various attack and defense methods implemented. It appears to be in active development, which is natural considering the area of research itself is also highly active. I've never tried using it, so I can't vouch personally for the extent to which it's easily usable as a tool for industry, or if it's maybe really only still suitable for research.



Based on comments by Ilya, other frameworks that may be useful to consider are Cleverhans and Foolbox.